2.4.2 Random forest (RF)
Breiman improved the regression tree model based on the Bagging
algorithm and proposed the random forest algorithm (BREIMAN, 2001) ,
which consists of sub-training sets and sub-regression models (decision
trees), which extracts m multiple sample data points from the
original sample set D through Bootstrap resampling method to form
a sub-training sample set with the same sample size as the original one
(see Figure 5 (b)). For each sub-training sample set, a
sub-regression model is constructed, which is called random forest model
(Das et al., 2017; Ibarra-Berastegi et al., 2015; Nashwan and Shahid,
2019) .